Method for the orientation, route planning and control of an autonomous mobile unit

ABSTRACT

Different bonus values and penalty values are allotted for each partial task of the unit such as, for example, drive from A to B, hold your positional uncertainty below a specific threshold, or draw up a map of the surroundings and add landmarks to it. Performance weightings for the individual tasks are yielded, in conjunction with a need to carry the latter out, after analysis of the bonus values and penalty values, and are evaluated in a control unit. Furthermore, in the context of the method a local planning horizon is specified in which the surroundings of the unit are subdivided into grid cells. Preferred directions, which lead the unit by the shortest path to already known or unconfirmed landmarks are stored for these grid cells, with the aim of reducing the positional uncertainty, or of being able to confirm a landmark. All the different routes which are possible within the framework of this grid are then investigated as to what contribution they make to enable the unit to reach the goal. In this process, the different costs and benefits per partial task are added up along each path. That route is selected which has the greatest benefit or the lowest loss. Finally, a destination which is situated outside the local planning horizon is reached by carrying out the method cyclically.

BACKGROUND OF THE INVENTION

The most varied fields of use can be conceived for autonomouslyoperating units. They are particularly suitable for use in danger areasand for remote sensing, but they are also capable of highly variedactivities in buildings. There, they can carry out activities such as,for example, industrial vacuum cleaners, transport vehicles in theproduction industry or, not least, as mobile multipurpose robots. Inexecuting these different activities, the autonomous mobile unit isconfronted, however, with the problem of having to draw up a map ofsurroundings which are at first unknown, and of being able to use thismap to locate itself at any given instant in its working environment. Tosolve this problem, such autonomous robots mostly have a controlcomputer and sensors by means of which they interact flexibly with theirenvironment. Examples of such sensors are laser distance scanners, videocameras and ultrasonic sensors.

The robot's operating procedure of orientating itself while travelingand simultaneously building up a map of the unknown surroundings posesthe problem that there is a mutual functional relationship betweendrawing up the map of the surroundings and locating the robot. Animportant role is played here by, in particular, the type and accuracyof the sensors which the robot uses to survey the path it has coveredand to locate obstacles in the surroundings. For example, the pathcovered from a starting point is determined with the aid of a wheelsensor. On the other hand, the distance from obstacles which occur ismeasured with the aid of distance sensors, and said obstacles areentered as landmarks in the map of the surroundings. Because of themutual functional relationship between the measuring procedure fordetermining the distance of obstacles and the procedure for measuringthe path distance covered in conjunction with drawing up the map andwith the errors which the measuring sensors have, these errorsaccumulate as a function of the path distance covered by the robot.

The autonomous mobile unit can therefore no longer be manipulatedsensibly from a specific limit.

A method which addresses this problem and indicates a solution for itwas advanced by W. D. Rencken in the article "Concurrent Localisationand Map Building for Mobile Robots Using Ultrasonic Sensors", Proc. ofthe 1993 IEEE/RSJ. International Conference on Intelligent Robots andSystems, Yokohama, Jap. Jul. 26 to Jul. 30, 1993, pages 2192 to 2197.The known measuring errors of the sensors used are used there for thepurpose of correcting a predicted landmark position, found with the aidof the internal map, as a function of a path distance covered. Theabsolute measuring error which occurs during the movement of theautonomous mobile unit is thereby reduced.

A further method for orientating self-propelled mobile units in unknownsurroundings consists in that the unit builds up a two-dimensional gridof its surroundings and provides individual cells of this grid withoccupancy values. The occupancy values assigned per grid cell representthe occurrence of obstacles in the surroundings.

Such a method is specified by the published document "Histogrammicin-motion mapping for mobile robot obstacle avoidance", IEEETransactions on Robotics Automation, Vol. 7, No. 4, August 1991, by J.Borenstein and Yoram Koren. It is described there how ultrasonic sensorscan be used to draw up a map of the surroundings of a self-propelledmobile unit.

The process of drawing up a map while the robot is possibly continuingto travel and being repositioned is time consuming and requires thecontrol computer to perform computations. This hampers the robot incarrying out an activity it has been assigned.

It is therefore extremely desirable for an autonomous mobile unit not touse too much time for orientation tasks when performing a task definedby the user. However, it is also important in this case that within thecontest of the task which has been set it can always maintain a definedmeasure of accuracy of orientation. This means, in other words, that thepositional error of the autonomous robot should not overshoot a certainlimit, otherwise said robot would no longer be able, for example, todeposit letters in the basket for incoming post when distributing post.

SUMMARY OF THE INVENTION

The object on which the invention is based therefore consists inspecifying a method for the orientation, route planning and control ofan autonomous mobile unit which enables the autonomous mobile unit totravel as short a distance as possible from a starting point to adestination, to perform a task defined by the user and, in the process,to monitor the positioning error. A partial object consists in improvingthe level of orientation with the aid of a grid array.

In general terms the present invention is a method for the orientation,route planning and control of an autonomous mobile unit. The unit drawsup a map of its surroundings in a first routine by using an on-boardsensor arrangement for surveying the surroundings. Starting from its ownposition the unit evaluates features of the surroundings which thusbecome known to it and enters them into the map of the surroundings inthe form of landmarks. In surroundings it does not know completely, theunit moves in a second routine from a starting point via at least onepartial goal in the direction of a destination. In so doing the unitmakes use of at least the map of the surroundings and the sensorarrangement for the purpose of orientation, route planning and control.

In a third routine the unit monitors the errors, present by virtue ofthe measuring inaccuracy of the sensor arrangement, in the determinationof its own position, as positional inaccuracy.

In each case at least one bonus value and/or penalty value is allottedfor each routine to be carried out, as a function of the contributionwhich they make in order to enable the unit to reach its destination.

As a consequence of a common evaluation of the respective bonus valuesand/or penalty values in a control unit of the unit at least the routineto be carried out is determined, and the route of the unit is plannedand controlled.

Advantageous developments of the present invention are as follows.

At least for one routine a weighting factor is obtained by adding up theassociated bonus values and/or penalty values and multiplying by anecessity value currently valid for this routine.

At least for each routine a threshold value is fixed for the bonusvalues and/or penalty values upon the overshooting or undershooting ofwhich the routine is carried out in a prioritized fashion.

In order to reduce the positional uncertainty, a landmark isdeliberately approached and surveyed, the unit knowing its location inthe surroundings with great accuracy.

At least one penalty value is a function of which path distance the unitmust cover to perform a routine.

At least the bonus value for the second routine is a function of theangle which is formed by a selected travel direction with thestart/destination axis.

Referred to elongated landmarks, at least the bonus value for the thirdroutine is a function of the magnitude of the segment projected on tothe normal of the landmark, which is produced by projecting a positionaluncertainty area around the site of the unit.

A landmark whose position in the surroundings of the unit is known onlyvery inaccurately is deliberately approached and surveyed.

The autonomous unit draws up a cellularly structured map of itssurroundings in the first routine, the landmarks being distinguished asconfirmed and unconfirmed landmarks as a function of the number ofmeasuring operations affecting them and/or of the number of thelocations from which they were surveyed. At least the followinginformation is stored per cell of the map of the surroundings:

i) seen from the cell, is a landmark located in the measuring range of adistance meter on-board the unit;

ii) if i) is answered affirmatively,

for confirmed landmarks: the direction of distance measurement along adirection between the affected cell and at least one confirmed landmark

for unconfirmed landmarks: the travel direction along at least oneunconfirmed landmark

iii) how often has the affected cell already been crossed.

A planning horizon is prescribed as a number of cells to be driventhrough in succession.

Possible travel directions of a unit are discretized such that, startingfrom a given cell position of the unit, each immediately adjacent cellis reached only in respectively one discrete travel direction.

For the planning horizon all the routes which can be combined using thediscrete travel directions are evaluated by adding up the respectivelyoccurring bonus values and/or penalty values in the control unit of theunit and that route is traveled which achieves the highest bonus valueor lowest penalty value.

The degree-of-occupancy values which can be incremented per cell arestored as a measure of the probability of the occurrence of an obstacle.

The cells are square and consequently eight travel directions areconsidered; alternatively, the cells are hexagonal, and consequently sixtravel directions are considered.

The combined routes are evaluated in a configuration space whose spaceaxes are bounded by the planning horizon in two axial directions. Thenumber of discrete travel directions is bounded in a third traveldirection. The same information stored in the cells is stored in thedirection of the travel direction axis for all respectively superimposedcells.

At least that route cell sequence which achieves the highest bonus valueand/or lowest penalty value is stored per cell of the configurationspace.

One advantage of the method according to the invention consists in thatbonus values and penalty values are allotted for each partial task to beperformed. These bonus values and penalty values depend on the extent towhich they serve the purpose of performing a task defined by the user,and the extent to which, and how optimally, the goal to be approachedcan be reached. It is particularly advantageous in this case that thepositioning error of the autonomous mobile unit is constantly determinedand included in the route planning and control of the autonomous mobileunit. When a specific threshold value for the positioning error isreached, it is therefore possible to introduce suitable measures withwhich this error can be reduced again.

In a favorable way, threshold values for the bonus values and penaltyvalues can be fixed for the most varied partial tasks which thisautonomous unit is to perform, so that it is possible as a function ofthese threshold values and their being overshot or undershot tointroduce special measures for performing this task or to perform thispartial task as a priority.

The method according to the invention advantageously provides, for thepurpose of reducing the positioning error, to steer the autonomousmobile unit to a known obstacle in its surroundings whose position isknown very accurately as a landmark in the unit's map of thesurroundings, to measure said position, and to use this measured valueto correct the unit's own location and the location of the landmark inthe map of the surroundings. In this way, the positioning error isreduced in a simple way precisely at the instant when required.

Since, when the unit is measuring the path, the errors which occur are afunction of the path distance covered, the method according to theinvention advantageously provides to render at least one penalty valuedependent on the path distance covered by the unit between a startingpoint and an intermediate goal or that point at which the lastorientation was carried out.

The method according to the invention advantageously further provides tocause the unit to travel as far as possible in the direction of aprescribed destination by allotting a bonus value as a function of theangle which the current travel direction forms with the prescribeddirection from the start to the goal.

The method according to the invention advantageously provides todetermine the bonus value which is yielded by approaching a knownelongated landmark in the surroundings by projecting the positionaluncertainty area on to the normal of this landmark and using the segmentproduced as a measure of the bonus value.

For the purpose of improving the orientation of the autonomous mobileunit, the method according to the invention advantageously provides toimprove the position of a not very accurately known landmark in the mapof the surroundings by deliberately approaching it and surveying it.Accurate knowledge of a plurality of landmarks in the map of thesurroundings makes it easier for a unit to reduce the positioning errorat the most varied points on its journey.

A particular advantage of the invention consists in that the task ofdrawing up the map is facilitated by subdividing the near surroundingsof the automotive unit into cells. The cells are subsequently occupiedby values which make it easier for the unit to perform its differentpartial tasks. It is then, for example, immediately clear whether anobstacle is located in the visual range of a measuring sensor, or howoften the cell has already been crossed, so that it can be assumedtherefrom that this cell is not helpful in the task of drawing up themap. Furthermore, the storage of preferred travel directions per cell ofthe map of the surroundings immediately makes it clear in whichdirection a landmark is located, irrespective of whether it is confirmedor unconfirmed, with the result that with regard to the task of drawingup a map and the task of minimizing the positional error it is possibleto approach a landmark appropriately, and the direction is accuratelyknown by means of which a sensible measurement result is obtained.

The method according to the invention can advantageously also becombined with already known orientation methods in cellularly structuredsurroundings, by introducing, in addition to the cell features claimedin claim 1 degree-of-occupancy values which specify a probability for anobstacle in the map of the surroundings, which leads to a shortercomputing time in the route planning of the automotive unit.

The map of the surroundings can advantageously be parcelled into squarecells, with the result that eight travel directions are possible andthere is a corresponding reduction in the computing outlay for routeplanning.

The cellularly structured map of the surroundings for the methodaccording to the invention can advantageously also have an hexagonalstructure, it thereby becoming possible to achieve a larger planninghorizon by comparison with the square grids, in conjunction with acomparable computing performance.

It is particularly favorable to consider a planning space in which thevarious discrete orientations of the vehicle make up one axis and the xand y axes make up the other axes. Each route can be distinguished fromanother in this planning space. It is advantageous for the items ofinformation which are to be stored per cell in accordance with claim 1or 2 subsequently to be superimposed in planes so that the obstaclesituation in the planning space is the same for every orientation of theunit.

It is advantageous to bound the planning space and to select routeswhich supply the most favorable bonus value or penalty value, since theyperform the partial tasks of the autonomous mobile unit with the mostsparing use of resources, for example. The outlay for calculating theroute can be limited by means of the planning horizon.

It is particularly favorable to compose the resultant benefit of variouspartial benefits and to weight these by means of priorities, since inthis way optimum route planning is rendered possible as a function ofthe weighting of the different partial tasks. In the task of drawing upthe map, it is favorable to consider the most varied factors, which alsoinclude how many landmarks are visible from a cell, what is therelationship of the current travel direction to the stored traveldirections, how often has this cell already been crossed, and to includethe ratio of the number of the confirmed and unconfirmed landmarks asweighting factor.

Two ways are advantageously available for calculating in accordance withthe invention the resultant benefit for an individual route inside acell by weighting the bonus value, yielded from the direction of thepositional error, additively, on one occasion, and multiplicatively, onthe other occasion. In practice, this produces good routes for theautonomous mobile unit.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the present invention which are believed to be novel,are set forth with particularity in the appended claims. The invention,together with further objects and advantages, may best be understood byreference to the following description taken in conjunction with theaccompanying drawings, in the several Figures of which like referencenumerals identify like elements, and in which:

FIG. 1 shows an example of implementation for the method according tothe invention.

FIG. 2 shows an autonomous mobile unit with accurately and lessaccurately known landmarks.

FIG. 3 shows an example of a route of an autonomous mobile unit insurroundings where there are obstacles, in conjunction with thepositioning error.

FIG. 4 shows an example of the route of an autonomous unit which isperforming the method according to the invention.

FIG. 5 shows an example of the allotting of a bonus value.

FIG. 6 shows an autonomous mobile unit in its surroundings.

FIG. 7 shows a cellularly structured map of the surroundings with aplanning horizon.

FIGS. 8a-8c shows examples for reducing the positional error.

FIG. 9 shows a route which is composed of discrete travel directions.

FIGS. 10a-10d shows an autonomous mobile unit and possible sequentialpositions, as a function of the discrete travel directions.

FIG. 11 illustrates the calculation of the bonus value for the taskdefined by the user.

FIGS. 12a-12b shows weighting functions for the visit counter of a cell,and the number of the unconfirmed landmarks detectable from the cell.

FIG. 13 shows the route of an autonomous mobile unit in surroundingsunknown a priori.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 shows as an example an architecture of the method according tothe invention, in the form of a block diagram. Represented here arethree tasks, the necessity factors of which are entered into thecorresponding boxes, which respectively symbolize the partial task.These are denoted by N_(u), N_(c) and N_(f). The index u is used herefor the task defined by the user, that is to say, for example, deliverpost, paint walls or do similar things, such as have already beenoutlined at the beginning in the technological background. Thepositioning uncertainty of the mobile robot is monitored in the secondtask, which receives index c. The third task, which receives index f,consists in supplying new landmarks to the map of the surroundings. Thisis performed by the autonomous mobile unit surveying the path it hascovered and using its sensors to determine the distance to obstacles inthe surroundings. These obstacles are subsequently entered as landmarksin the map of the surroundings which the autonomous unit has on-board.

The individual partial tasks are assigned bonus values and penaltyvalues, denoted by B and C respectively, in the representative exampleof architecture. These values yield the necessity to perform a taskwhich leads to a weighting factor R, which is evaluated in the controlunit opti and from which, for example, an intermediate goal tar and anexpectation value exp are calculated. In this example, necessity factorsN_(u), N_(c) and N_(f) are additionally allotted for the individualtasks. In order to determine the weighting factor R, it is possible, forexample, to form the difference between the bonus values and penaltyvalues for the respective partial task and to multiply it by thisnecessity factor N to form a weighting factor R.

    R(φ)=N(B·C)

Several partial processes can also be evaluated by the control optiwithout limiting the controller. All that is required is to determinethe bonus values and penalty values for the individual partial tasks asa function of the partial tasks set, and it can also be useful here, asthe case may be, to fix a necessity factor for performing such a partialtask. The bonus values and penalty values for the respective partialtask are selected in a sensible way so that maneuvers such as contributeto performing the task, defined by the user, of the mobile unit U areprovided with bonus values, and those such as, for example, divert themobile unit from its travel direction or delay the temporal sequence ofthe activity are provided with penalty values. It is possible, forexample, to favor directional deviations from the prescribed start/godirection which are very small with a bonus value, but it is alsopossible to disadvantage positional uncertainties, which overshoot aspecific magnitude, with a penalty value. It is also conceivable toavoid a long route between two intermediate goals on the way from thestarting point to the destination by means of large penalty values. Itis also conceivable that in the case of surroundings which contain manyobstacles of which only a few are accurately known in the unit's map asuitable allocation of bonus values and penalty values will force theunit to survey correspondingly more obstacles and enter them in the mapof the surroundings as landmarks. Basically all influencing factorswhich are related to the journey of the autonomous robot or to carryingout the individual activities with which it is commissioned are suitablefor allotting bonus values and penalty values. For example, it can alsobe favorable to evaluate the time response, the driving speed, or thepower consumption of the mobile unit.

Suitable for fixing the necessity factor N_(c), which controls thepartial task of a unit to keep the positional uncertainty low, is, forexample, the area A which is formed by the positional uncertainty aboutthe current location of the mobile unit. In other words, the area meantby this is that which, as a function of the sensor measuring errorswhich accumulate over the journey by the unit, specifies a probablestopping place around a position of the unit which is currently enteredin the map.

For example, the area can be used as a measure of this necessity tocorrect the positional uncertainty. If the area undershoots a minimumvalue min, this partial task can be prevented from being activated, forexample; and if this specific area overshoots a specific value max,performance of this partial task can be enforced. In the intermediaterange, it is recommended, for example, to relate the current area to thedifference between the minimum and the maximum, in order thus to obtainin a linear fashion as a function of the currently present positionaluncertainty area a necessity factor for the performance of the positioncorrection. It then holds, for example, that: ##EQU1##

The necessity factor N_(u) for performing the task defined by the usercan, for example, be rendered a function of how much of this task hasalready been performed. A high factor can be allotted at the start ofthe activity and be reduced toward the end of the activity. For example,for the bonus values in this case it holds that: ##EQU2## with φ: anglebetween the start/goal direction and the current travel direction.

As the calculating basis for determining the necessity N_(f) of adding anew landmark to the map, it is possible to use, for example, the numberof the absolutely detected obstacles in conjunction with the number ofalready confirmed obstacles in the map. The ratio of the defined knownlandmarks and inaccurately known landmarks represents a sensible measureof how accurately the unit can currently orientate itself, and thusleads to a sensible weighting factor for the task of drawing up the map.It holds for the penalty value which is yielded when an inaccuratelyknown landmark is approached that, for example: ##EQU3## with: d:current driven distance of the unit

D₋₋ max: maximum permissible driven distance.

A possible strategy for planning and driving an optimum route can be toselect a direction which fulfills as many sub-goals, that is to saypartial tasks, as possible. However, it can also be sensible, forexample, initially to perform that task which receives the maximumweighting factor. For sensible route planning, however, it can also besensible to select only those intermediate goals which are locatedwithin an angle of 90° between the start/goal direction and the currentdirection of movement, in order to deviate as little as possible fromthe travel direction to the goal.

If, for example, the weighting maximum is yielded from the task definedby the user, the distance to an intermediate goal is maintained, forexample. In the other cases, in which the weighting factors of the otherpartial tasks dominate, the path into the visibility region of anobstacle whose position is to be determined is determined, for example,as the distance and serves for calculating the destination coordinatesfor the intermediate goal. Visibility region means the range in whichthe distance meter sensor can determine the position of an obstacle,that is to say its distance, with high accuracy. For example, in thecase of the task of drawing up the map or of the task of positioncorrection an intermediate goal is selected here which is located at theedge of the visibility region of an obstacle.

FIG. 2 shows a scenario as an example for an autonomous mobile unit AEin its surroundings. It can be seen that here the position of a unit inthe map of its surroundings and the known obstacles are represented asthey are contained in the memory of the unit's controller. The startingpoint ST and a destination go are shown. R_(max) denotes, for example,an observing horizon or an evaluation horizon for control measures andintermediate goals of the unit. Furthermore, a coordinate system xy isshown, and obstacles 1 to 4 are present. The obstacles 1 and 2 are, forexample, entered as confirmed landmarks in the map which the autonomousmobile unit draws up and uses for orientation. That is to say, thecoordinates of these obstacles have been measured frequently and are nowfixed with high accuracy. For reference purposes, for example in orderto correct the positional uncertainty, these obstacles 1 and 2 can beapproached and surveyed with a sensor. The specific distance and thecurrent knowledge of the location of the obstacles in the map serves thepurpose of reducing the positional uncertainty. The obstacles 3 and 4 donot represent any such confirmed landmarks, rather their location isaffected by an inaccuracy. For example, these obstacles have beendetected on the edge of the visibility region of the sensors and havenot yet been more accurately surveyed. A first measurement has, however,led to these obstacles being entered in the map of the surroundings.

The individual visibility regions vis are shown for the individualobstacles, both the confirmed ones 1 and 2 and the unconfirmed ones 3and 4. These visibility regions are described in the form of rectanglesvia around the landmarks. A visibility region results from the detectionrange of the sensors and the dependence of their measuring accuracy as afunction of the distance which the sensor measures and of the desiredaccuracy for confirmed landmarks. In order to be able to determine thelocation or the distance of such an obstacle 1 to 4, the sensor, whichis mounted on the self-propelled mobile unit AE, must be located atleast at the edge of such a visibility zone vis. It is then possible tomeasure the distance exactly. For the purpose of illustration, apositioning uncertainty area is drawn around the autonomous unit AE.This area PU has the form of an ellipse. As is to be seen further, thisellipse is not arranged symmetrically about the unit. Rather, the angleof rotation of the ellipse in relation to the longitudinal axis of theunit AE results from the fact that before the point ST was reacheddifferent driving maneuvers had been carried out in the foreground, andhave led in the different directions xy of the coordinate system to adiverse accumulation of measuring inaccuracies.

Also entered in FIG. 2 is an intermediate goal tar. The distance betweenthe starting point ST and the intermediate goal tar is denoted by d. Theangle which the selected travel direction to the intermediate goal tarmakes with the start/goal direction ST-go is denoted by φ. A bonusvalue, which is used in the method according to the invention, can berendered dependent on this angle φ, for example. It would be sensiblenot to let this angle become larger than 90°, since otherwise there isno movement in the direction of the goal. In the example in FIG. 2, anintermediate goal has been selected which is located at the edge of thevisibility region vis of a non-confirmed landmark 3. That is to say, theresult of evaluating the individual partial tasks in conjunction withthe bonus values and penalty values is that the task N_(f) of drawing upthe map is to be carried out. As a function of the necessity factorsN_(u), N_(c) and N_(f) in conjunction with the weighting factors R₀ toR_(n+m), the control unit opti has determined this intermediate goal tarin order to approach it next. Furthermore, an expected intermediatedestination exp is stored in the unit's memory on the basis of thepositional uncertainty. Such a driving maneuver of the autonomous unitcan be sensible, for example, whenever sufficient information on thesurroundings is no longer present, or when the positional uncertaintyhas risen sharply and no confirmed landmarks are present any more in thevicinity of the autonomous unit. In the longer term, the result of thiswould be that the positional uncertainty can be further increased andthe path to the goal go can no longer be found, since new landmarks canno longer be accurately determined and the current position of a unitrelative to the control computer is no longer known.

FIG. 3 shows as an example the path covered from the starting point STto the destination 2 of an autonomous unit AE. The method according tothe invention is not applied in this example. Individual landmarks LMare detected on the path to an intermediate goal 1 and, as representedin FIG. 3, the autonomous unit AE drives directly to the intermediategoal 1 without going into the visibility zones via of the individuallandmarks LM in order, for example, to correct the positionaluncertainty. This positional uncertainty results from the inaccuracy ofthe sensor measurements and the accumulation of the error of theindividual sensors over the path distance covered by the autonomousunit. Having arrived at the goal 2 of its journey, the autonomous unitaccordingly has a very high positional uncertainty PU, represented herein the form of an ellipse. Here, this means that for the controlcomputer of the autonomous unit the unit is located at the goal 2, butthat the positional uncertainty has increased to form the ellipse PUowing to the increased measuring errors of the individual sensors of themobile unit. Although the current location of the unit is thereby markedin the map, any stopping place inside the positional uncertainty area PUis possible for the unit. In conjunction with the performance of a taskdefined by the user, it can easily be seen that, owing to the largeerror characterized by the positional uncertainty, the unit can nolonger be positioned exactly.

FIG. 4 shows the route of an autonomous unit AE through surroundingsobstructed by obstacles between a starting point ST and a destination 2.The surroundings are in this case the same as those in FIG. 3. Here, aswell, landmarks LM and visibility regions vis are represented again inthe surroundings. By contrast with the mode of procedure in FIG. 3, herethe method according to the invention is used in the route planning andcontrol of the autonomous unit. It is clear to see that the autonomousmobile unit no longer drives directly to the intermediate goal 1 andthen immediately to the end point 2, but rather that the unit's routeleads through a plurality of visibility regions vis of landmarks. Thisroute is produced, for example, when a partial task of the unit consistsin keeping the positional uncertainty below a specific threshold value.As can be seen, the positional uncertainty overshoots this thresholdvalue at the points A, B, C and D. By weighting the individual necessityfactors in conjunction with the weighting factors N and evaluating theindividual bonus values and penalty values B, C, the control unit takesthe decision to proceed at these points in the direction of thevisibility region of a known landmark LM. By driving into the visibilityregion of such a landmark, the latter can be surveyed exactly and theseparation thus obtained can be compared with a value which results fromthe current position in the map and the stored position of the landmark.The absolute positional uncertainty can be reduced in this way with theaid of this measurement. As it is easy to see, the repeated applicationof this method produces at the end of the route at the destination 2 amuch lower positional uncertainty PU than was the case for FIG. 3. Thus,the autonomous unit can perform a task defined by the user with a higherexactitude. At the same time, however, the method according to theinvention limits the quantity of error correction measures to thesmallest possible extent. This results in an optimum time response inperforming a task defined by the user by the autonomous mobile unit.

FIG. 5 illustrates the example for allotting a bonus value whenapproaching an elongated obstacle LM for the purpose of positionalcorrection of the autonomous mobile unit. The unit is situated at thelocation Z in front of the confirmed landmark LM. During the journey ofthe mobile unit, the positional uncertainty has been added up to producean ellipse with the semiaxes b and a. The semiaxis a forms the angle twith the surface normal to the elongated obstacle LM, which is denotedby P_(r), and the semiaxis B forms the angle α with it. The projectionof the area of the ellipse on to the surface normal of the landmark LM,the surface normal being denoted by P_(r), produces the distance W_(c)as a measure of a bonus value for performing the task of positionalcorrection. Only half the distance which would be produced by projectingthe ellipse on to the surface normal is represented here. However, sincein this example the bonus value is selected to be proportional to thisdistance, only a factor 2 is concerned here and not a qualitativedifference.

The position of the unit relative to a given instant k can be specifiedaccording to Rencken as follow:

    x(k)= x(k),y(k)!.sup.T

The covariance matrix: ##EQU4## with: σ_(x) uncertainty in thex-direction

σ_(y) uncertainty in the y-direction holds in this case for thepositional uncertainty.

The map M(k) of the surroundings, which is composed from a map forconfirmed landmarks M_(c) (k) and unconfirmed landmarks M_(t) (k) can beformalized in this case as follows:

    M(k)={(P.sub.f (k), Λ.sub.f (k), vis)|1≦f≦n.sub.f }=M.sub.t (k)∪M.sub.c (k)

with:

Λ_(f) (k) uncertainty of landmark p_(f)

vis visibility region

n_(f) number of landmarks

FIG. 6 shows the surroundings U of an autonomous unit AE. A planninghorizon PH of the autonomous mobile unit is located in the surroundingsU. Landmarks LM1 to LM3 are to be seen inside the planning horizon. Thegoal 2 of the autonomous mobile unit is located inside the surroundingsbut outside the planning horizon PH. In order to reach the goal 2, inaccordance with the method according to the invention the unit mustfirstly structure the region in the planning horizon in a cellularfashion and explore a route inside this planning horizon. This routeleads to a partial goal 1. During its journey through the surroundings,the unit can, for example, carry the planning horizon with it by alwayscarrying with it the center of the cellularly structured map of thesurroundings and, as it were, shifting the planning horizon over thesurroundings like a window.

FIG. 7 shows a cellularly structured map of the surroundings inside aplanning horizon PH. The unit is located in this case at the center ofthe map in a starting cell S. Various landmarks LM1 to LM4 are enteredinside the map of the surroundings and the planning horizon PH. LM1 andLM4 are edges in this case. LM3 and LM2 are linear extending landmarks.

Directional arrows 71 and 72 are specified for the landmarks LM1 and LM2and symbolize a preferred measuring direction in which the unit is tomeasure in order to be able to survey the respective landmarkaccurately. According to the method of Rencken, it is possible in thisway to reduce the positional error of a unit. As can further be seen,these arrows are stored inside cells which are located in the visibilityregion of the respective landmark. Visibility region means in thiscontext that the landmark can still just be detected reliably by adistance meter of the unit. These cells are represented in dark gray inFIG. 7 here. The information specified here, which is stored per cell,is not in any way to be regarded as a complete enumeration; it isentirely conceivable that other information which is favorable fororienting the autonomous mobile units can also be stored as cells. It isalso conceivable to select a coarser or finer cell array. For example,planning level 5 has been selected here in order to reduce the outlay oncomputation inside the control unit of the autonomous mobile unit to anacceptable amount.

Preferred travel directions, which are denoted here by 73 and 74, arealso stored for the cells which are located in the visibility region oflandmarks LM3 and LM4. Since LM3 and LM4 are landmarks which have notyet been confirmed, these directional arrows point along the landmarks,in order in this case to make it easier for the autonomous mobile unitwhich covers this route to confirm or reject a landmark. If a routewhich is planned crosses a cell occupied by a travel direction, it ispossible to evaluate how the alignment of the unit behaves in relationto the stored travel direction, and this relationship can serve as ameasure of the fixing of a bonus value for this current route.

The method according to the invention assumes in principle that it ispossible to reach a specific remote goal (for example, drive to aspecific point which is very far removed, explore a building complex).However, it is only paths which have a few seconds driving time andrequire a planning horizon of a few meters which are explicitlyplanned/searched for in advance.

The information gathered during the journey and relating to therelatively near surroundings of the autonomous mobile unit, such asobstacles and confirmed and provisional landmarks are stored in a localrepresentation of an environment. A local path planning method is thenused, for example, to search for an optimum local route. The search isperformed, for example, by estimating the expected costs and benefitsalong this route, starting from the instantaneous state of the unit.Costs may be regarded, for example, as the path length or the magnitudeto be expected of the positional uncertainty. Benefits are, for example,finding new landmarks, approaching a destination prescribed by the user,or reducing the positional uncertainty. When setting up the costfunctions and benefit functions use is made, for example, of heuristicswhich are yielded from observing and analyzing the localization processused.

The optimum partial path thus found is then, for example, traveled by acontrol unit of the autonomous mobile robot, it being possible forunexpected obstacles to be automatically bypassed, for example. Duringthis journey, it is constantly monitored, for example, whether theexpectations set during planning (for example, behavior of thepositional uncertainty) are also actually fulfilled. In the case ofsubstantial deviations, which are possible at any time owing to theincompleteness of the knowledge of the surroundings, new planning isinitiated, for example. Otherwise, after the planned path has beentraveled the search for a path is begun anew and a further segment isplanned in local surroundings. This process is performed, for example,cyclically until the prescribed remote goal has been reached.

In order to discretize the search space, the relatively nearsurroundings of the autonomous mobile unit (a few meters) around thecurrent position of the autonomous mobile unit are subdivided into gridcells. These are located, for example, at the center of this grid. Thegrid cells are occupied by attributes which reflect the informationabout the surroundings. For example, during each planning operationcurrent information from different sources is assigned to the localplanning grid. The following attributes, for example, are stored foreach grid cell.

1. Whether a landmark is located in the visibility region. Thevisibility region is a function of the range and the characteristics ofthe sensors. For example, rectangles or circular segments can be takenfor lines or point landmarks. Other visibility regions are conceivable.If the sensors are able, for example, to detect landmarks only at aspecific distance, this could result in narrow bands as visibilityregions.

The positional uncertainty can be reduced in such cells, which aresituated in the visibility region of one or more confirmed landmarks.For example, the match direction is stored for each landmark. For anelongated landmark LM2, 72, for example, this direction is at rightangles to the wall. In the case of point landmarks LM1, however, thematch direction is situated, for example, on the connecting straightline from the cell center point to the landmark 71. If exactly onelandmark can be seen from a cell, the positional uncertainty can bereduced only in the match direction. If a plurality of landmarks can beseen, the uncertainty ellipse is compressed in a plurality ofdirections. The rotational uncertainty can also be reduced by aplurality of landmarks.

FIG. 8 shows three situations and positional uncertainty correctionsassociated therewith. An elongated landmark LM1 is to be seen in parta). A third measuring direction R1 is at right angles to it. Thismeasuring direction R1 is stored, for example, in those cells which arelocated inside the visibility region of a distance measuring probemounted on a movable vehicle when said probe has just detected thelandmark LM1. For example, within the scope of the various routineswhich are carried out by the autonomous mobile unit, it can be necessaryto carry out a positional correction and again to reduce the positionalerror which has been increased, for example, by odometry errors. Themethod of Rencken provides for surveying a landmark accurately with theaid of the distance measuring sensors, and for determining a positionfor correction purposes via the known initial position of the unit andthe change in position resulting from the path distance covered. Beforearriving in the visibility region of the landmark, the positionaluncertainty is given, for example, by an ellipse PUV. By moving theautonomous mobile unit in the direction R1, that is to say at rightangles to the landmark, the positional uncertainty is reduced in thisdirection. Finally, the positional uncertainty ellipse PUN is produced.

FIG. 8b) shows the same case. However, two landmarks LM1 and LM2 arepresent here. Shown at right angles thereto are corresponding preferredmeasuring directions R1 and R2 in which a self-propelled mobile unit canperform a positional correction. It is not necessary for this purposethat the unit measures or drives exactly in these directions, but inthis way an optimum error correction is achieved through a minimumextent of outlay. By comparison with 8a), it can be seen that thepositional uncertainty ellipse PUV shrinks to a very small ellipse PUN,since it is possible here to perform a positional correction in twoorthogonal directions, the ellipse being compressed in both directionsas a result. This renders it possible that the benefit for a positionalcorrection is a function of the number of the confirmed landmarks.

FIG. 8c) illustrates the positional correction in conjunction with apunctiform landmark LM4. The visibility region can be configured asfollows, for example, around such a landmark. The maximum measuringrange of the sensor is fixed as a distance, and this distance serves asradius for an arc which is drawn around LM4 as center. This produces acircular arc. Located between this circular arc and LM4 are cells forwhich radially extending optimum travel directions for detecting thelandmark LM4 can be stored. It can be seen that the positionaluncertainty PUV can be reduced with the aid of such a landmark just aswith the aid of an elongated landmark. The positional uncertaintyellipse PUN is obtained after the correction.

For example, a preferred travel direction for the autonomous mobile unitis stored for each cell which, referred to the distance measuring sensorof the unit, is located in the visibility region of an unconfirmedlandmark. It is assumed in this case that the landmark can be optimallysurveyed in this direction in particular. If the unit extendslongitudinally and a plurality of distance measuring sensors arearranged parallel to the longitudinal sides, it is possible, forexample, to detect walls particularly well when a preferred directionextends parallel to the wall. In addition, information can be stored onwhether a cell is located in the visibility region of a provisionallandmark. If this is the case, a preferred travel direction is stored insuch a cell and is intended to have the result that the landmark can bequickly confirmed or finally rejected. In the case of a wall-shapedlandmark, such a favorable travel direction (confirmation direction) issituated, for example, parallel to the wall. However, a corner can beconfirmed particularly well as a punctiform landmark, when the unit goesround it on a circular path. Other criteria (for example, preferreddistance from the landmark) are also conceivable, depending on the typeof the distance measuring sensor. For example, as also described byBorenstein and Koren, degree-of-occupancy values can be allotted to thecells, and the cells can be stored there. These degree-of-occupancyvalues specify the probability of stay for an obstacle in this cell.

In addition, it is possible to keep for each cell a counter in which itis noted how frequently the unit has already crossed this cell. When newlandmarks are being sought, it is possible subsequently to prefer, forexample, those cells which have not yet been crossed so frequently bythe unit.

FIG. 9 shows the planned route FW for an autonomous mobile unit AE. Theplanning horizon N is seven cells here. This route FW is selected in thesurroundings, within the scope of the local planning horizon and of thegrid, in such a way that the tasks being performed by the unit lead to apenalty value which is as small as possible or to a large bonus value.For example, preference is given to a route such as delivers aparticularly high bonus value. Such routes FW bring together thecombination of a plurality of partial tasks in a particularly optimumway.

Different orientations of the vehicle can be prescribed discretely, inorder to ease the calculation when planning the route. In the case whena square basic grid has been selected, eight cells surround a centralcell, for example an initial cell in the case of a movement operation ofa mobile unit. A sensible discretization thus represents here an angleof 45° between the individual orientations. Route planning thereforerequires eight cases to be investigated in each case, starting from astarting cell and going to a neighboring cell. In the case of anhexagonal grid, for example, only six neighboring cells would arise and,consequently six discrete travel directions, which in each case form anangle of 60° with one another.

FIG. 10 shows examples of travel directions in a square basic grid. Anautonomous mobile unit AE at the center of such a grid structure is tobe seen in FIG. 10a. FIG. 10b shows as an example the changes in traveldirection which such a unit can perform and which are to be investigatedwithin the scope of route planning from one cell to the next cell. FIGS.10c and d show another example. In comparison with FIG. 10a, the initialposition was selected differently in FIG. 10c, and this then entailsdifferent subsequent positions in FIG. 10c. It is important in this caseto bear in mind in the case of transfer from one cell to another cellthe travel direction can either be changed in a discrete step or can bemaintained. Furthermore, it is important to take into account that theunit can drive both forwards and backwards, so that it is possibleoverall by driving backwards and forwards to reach these eightneighboring positions from the initial cell.

A search is made for the route through the local planning grid, forexample after the grid cells have been assigned. An attempt is made inthis case to do justice to the tasks according to the user in thesequence of their prescribed priorities. The aim is in this case to finda path which entails a resultant benefit which is a maximum at theplanning level N. The required computing time for the path search isdecisively influenced by the planning horizon N. The planning horizon Nis preferably between 10 and 20. A grid size of, for example, 30×30 cmresults in a planning horizon of 3 to 6 m.

The search for the optimum path starts, for example, in the center cellof the local grid with the appropriate orientation of the autonomousmobile unit. All the end configurations after search step i serve asinitial points for the search step (i+1). As an example, the maximum ofeight possible subsequent configurations are investigated in each casefor these. Nine resulting benefits R_(N) are determined from this ineach case. Thus, in route planning a new route is searched for in such away that the cells searched through propagate like waves in theconfiguration space, starting from the starting cells. After N searchsteps, the path is followed back to the starting cell for the routehaving the highest resultant benefit R_(N). The resultant benefit can bea function, in this case, of the evaluation function B of the variouspartial tasks, and of the quasi-collected benefit along the path.

    R.sub.N =ƒ(B.sub.u,B.sub.c,B.sub.ƒ)

A resultant benefit function has the following appearance in principle:

    R.sub.N =α.sub.u *B.sub.u +α.sub.c *B.sub.c +α.sub.ƒ *B.sub.ƒ

Benefits are collected and weighted in accordance with the prioritiesfor all the partial tasks along the tested path. Costs can also arisealong the path (for example, positional uncertainty increases, powerconsumption), which can be subtracted from the selected benefits. At theend, that path is selected which offers the highest benefit with thecosts subtracted. The evaluation criteria for the individual partialtasks are described in the following tasks.

FIG. 11 shows for example how a cost/benefit examination can beconducted for a task defined by the user. The unit is located, forexample, in the cell ST. For example, B is the destination which hasbeen prescribed by the user, and P the next partial goal within thescope of route planning. The vectors b and p in this case specify theappropriate directions to the respective points B and P.

In order to be able to specify a benefit, the deviation of every pathsegment from a direction to the destination is evaluated for the taskdefined by the user, for example. The partial benefit for the respectivechange in travel direction is therefore a function of the angle definedby the two vectors. For example, the smaller this angle, the greater thebenefits with respect to the task defined by the user. The resultantevaluation factor, that is to say the partial benefit, is at a maximum,for example, when the unit is always moving in the direction of thedestination, that is to say in the b direction. ##EQU5##

In order to be able to evaluate the positional uncertainty, it isnecessary to determine the uncertainty at the end of a path of length N,that is to say the planning horizon. In order to be able to fulfill thepartial tasks of the unit, this positional uncertainty must be as smallas possible at the end of the path. Although it may increase along thepath, it should not overshoot a specific value. The evaluation functionof the configuration uncertainty is, for example, a function only of theuncertainty at the end of a path of length N. This is to be small at theend of a path; along the path, the uncertainty may also be larger. Thetrace sp(Q) of the uncertainty covariance matrix Q is defined as theevaluation criterion. ##EQU6##

The higher the configuration uncertainty, the smaller, for example, isthe benefit at the end of this path. For example, the benefit is notadded on here. Only the estimated uncertainty at the end of the path isused for the purpose of evaluation.

FIG. 12 shows two evaluation functions which are used within the scopeof the partial task of forming the map. In the task of building uplandmarks, benefits can be collected along the path, that is to say fromgrid cell to grid cell, when searching for the path (as in the case ofthe task defined by the user). In this case, the benefit per grid cellcan be composed as follows:

    N.sub.ƒ =(V.sub.i +T.sub.i +O.sub.i)*C.sub.i ##EQU7##

For example, the benefit of a cell which has just been visited dependson how frequently this cell has already been crossed by the unit. Thedependence can, for example, be as is specified in FIG. 12b. The numberof passing operations is denoted there by Z_(u). For example, the numberZ_(u) is increased by 1 for each crossing of a cell. The more frequentlythis cell has already been crossed, the lower is its benefit in buildingup the map, since any possible landmark present must already have beenfound. Using this function, which is represented in FIG. 12b and denotedby V_(i), the autonomous mobile unit is driven into an unknown area,since only there can a benefit be acquired. This favors the finding ofunknown landmarks in the new area. It takes the following form, forexample: ##EQU8##

A further factor which should be taken into account in the task offorming the map is the unconfirmed landmarks. FIG. 12a indicates afunction T_(i) which describes the benefit as a function of a number ofvisible provisional landmarks. The function states, for example, thatthe benefits of a cell under search becomes higher the more unconfirmedlandmarks can be seen from this cell. It is therefore assumed that theunit has already been in this area previously and has stored the data ofprovisional landmarks in its memory. However, since the landmarks havenot yet been surveyed frequently enough, they have not yet beenconfirmed. When building up the landmarks, it is therefore of greatbenefit to drive into the visibility regions of the provisionallandmarks, since there is a high probability in these areas of findingnew confirmed landmarks which can subsequently be used to perform apositional correction again. The unit therefore collects large benefitswhen it drives through visibility regions of provisional landmarks. Inqualitative terms, T_(i) has the following form, for example: ##EQU9##

The functions which are represented in FIGS. 12a and 12b tell only thequalitative relationship, and are not intended to be absolutelimitations on the invention. They represent the current state ofdevelopment. Furthermore, preferred orientations can be stored for allcells in the visibility region of provisional landmarks. In routeplanning and route searching, account is taken of the correspondencebetween the current orientation and the preferred orientation, which isstored in the cell. If the orientation of the unit corresponds to thepreferred direction, there is, for example, an additional benefit. Ifthe number of corresponding directions is greater, the benefit which isthereby achieved also becomes greater, for example. Furthermore, it canbe advantageous to determine a partial benefit in the form in which thenumber of already confirmed landmarks which are visible per cell is usedas a weighting factor. Specifically, it is possible to proceed in thiscase such that landmarks already confirmed restrict the benefit forbuilding up the map, since a plurality of confirmed landmarks which canbe seen from a cell suffice to render the position of the autonomousmobile unit capable of being exactly determined. It would therefore notbe necessary to build up further landmarks and thus waste computing timeand storage space.

The following evaluation formula is recommended for the individualpartial benefits of a route in the control unit:

    R.sub.i =α.sub.u *B.sub.ui +α.sub.ƒ *B.sub.ƒi +α.sub.c *(1-B.sub.ci)

    R.sub.i =(α.sub.u *B.sub.ui +α.sub.ƒ *B.sub.ƒi)*B.sub.ci

For example, the map can be evaluated overall by evaluating theoverlapping of landmarks in visibility regions. It is possible, forexample, to proceed as follows in this case: regions in which only onelandmark can be seen should be more closely investigated in order tofind further landmarks. Regions from which two landmarks can be seenhave been sufficiently explored, and it is not necessary to explorefurther landmarks. Such regions from which it has not yet been possibleto detect a landmark, must be searched specifically for any naturallandmarks which may be present. For example, given surroundings are notcompletely opened up until it is possible to see a plurality oflandmarks from each point. Completeness is not, however, mandatory. Anautonomous mobile unit can certainly orientate itself within the scopeof the start/goal direction in surroundings which are only known aroundthe route. In order to spare the resources of the autonomous mobileunit, it is possible to evaluate overlaps between the landmarkvisibility regions.

FIG. 13 shows the route of an autonomous mobile unit from a startingpoint ST to a goal 2. In detail, various landmarks LM are contained inthe surroundings, and the route FW passes by them. Also represented arethe visibility regions VIS of various characteristic points andlandmarks. It is to be borne in mind when viewing FIG. 13 that thesurroundings of the unit are not known at the start of its route at thestarting point ST. In order to fulfill the most varied partial tasks,grid cells are built up along the route and in a local area and areassigned preferred travel directions. The partial tasks are evaluated inaccordance with different criteria and weighted with the aid ofpriorities. For example it can be seen that at a location KO theautonomous mobile unit has driven in the direction of a landmark inorder to reduce the positional uncertainty. At a second location ST, itcan be seen that fewer landmarks are present there, and that thereforethe unit drove in the direction of various landmarks in order to reducethe positional uncertainty or to add new landmarks to the map.

The invention is not limited to the particular details of the methoddepicted and other modifications and applications are contemplated.Certain other changes may be made in the above described method withoutdeparting from the true spirit and scope of the invention hereininvolved. It is intended, therefore, that the subject matter in theabove depiction shall be interpreted as illustrative and not in alimiting sense.

What is claimed is:
 1. A method for orientation, route planning andcontrol of an autonomous mobile unit, comprising the steps of:in a firststep the unit drawing up a map of its surroundings in a first routine tobe evaluated by using an on-board sensor arrangement for surveying thesurroundings and, starting from a position of the unit, evaluatingfeatures of the surroundings which become known to the unit and enteringthe features into the map of the surroundings in a form of landmarks; ina second step, in surroundings the unit does not know completely, theunit moves in a second routine to be evaluated from a starting point viaat least one partial goal in a direction of a destination and in sodoing makes use of at least the map of the surroundings and the sensorarrangement for orientation, route planning and control; in a third stepin a third routine to be evaluated the unit monitors errors, due tomeasuring inaccuracy of the sensor arrangement, in determination of aposition of the unit, as positional inaccuracy, and reduces the errorsby approaching a landmark; in a fourth step at least one bonus valueand/or penalty value is respectively allotted for each routine of thefirst, second and third routines to be carried out, as a function ofcontribution which the at least one bonus value and/or penalty valuemake in order to enable the unit to reach its destination whereby atleast one destination direction deviation and/or a great positioningerror and/or a slight plurality of landmarks are poorly evaluated in themap; in a fifth step as a consequence of a common evaluation ofrespective bonus values and/or penalty values in a control unit of theunit at least the routine to be carried out is determined, and a routeof the unit is planned and controlled.
 2. The method as claimed in claim1, wherein at least for one routine to be evaluated of the first, secondand third routines a weighting factor is obtained by adding upassociated bonus values and/or penalty values and multiplying by anecessity value currently valid for the at least one routine.
 3. Themethod as claimed in claim 1, wherein at least for each routine to beevaluated of the first, second and third routines a threshold value isfixed for the bonus values and/or penalty values upon overshooting orundershooting of the threshold value which the at least one routine iscarried out in a prioritized fashion.
 4. The method as claimed in claim1, wherein in order to reduce positional uncertainty a landmark isdeliberately approached and surveyed, the unit knowing its location inthe surroundings with great accuracy.
 5. The method as claimed in claim1, wherein at least one penalty value is a function of a path distancethe unit must cover to perform a routine to be evaluated.
 6. The methodas claimed in claim 1 wherein at least the bonus value for the secondroutine is a function of an angle which is formed by a selected traveldirection with a start/destination axis.
 7. The method as claimed inclaim 3, wherein, referred to elongated landmarks, at least the bonusvalue for the third routine is a function of a magnitude of a segmentprojected on to a normal (p_(r)) of the landmark, which is produced byprojecting a positional uncertainty area around a site of the unit. 8.The method as claimed in claim 1, wherein a landmark whose position inthe surroundings of the unit is known only very inaccurately isdeliberately approached and surveyed.
 9. A method for orientation, routeplanning and control of an autonomous mobile unit, comprising the stepsof:in a first step the unit drawing up a map of its surroundings in afirst routine to be evaluated by using an on-board sensor arrangementfor surveying the surroundings and, starting from a position of theunit, evaluating features of the surroundings which become known to theunit and entering the features into the map of the surroundings in aform of landmarks, the autonomous unit drawing up a cellularlystructured map of surroundings of the unit in the first routine, thelandmarks being distinguished as confirmed and unconfirmed landmarks asa function of the number of measuring operations affecting the landmarksand/or of the number of the locations from which the landmarks weresurveyed, and at least the following information being stored per cellof the map of the surroundings:i) seen from the cell, is a landmarklocated in a measuring range of a distance meter on-board the unit; ii)if i) is answered affirmatively,for confirmed landmarks: a direction ofdistance measurement along a direction between the affected cell and atleast one confirmed landmark; for unconfirmed landmarks: the traveldirection along at least one unconfirmed landmark; iii) how often hasthe affected cell already been crossed;in a second step, in surroundingthe unit does not know completely, the unit moves in a second routine tobe evaluated from a starting point via at least one partial goal in adirection of a destination and in so doing makes use of at least the mapof the surroundings and the sensor arrangement for orientation, routeplanning and control, in a third step in a third routine to be evaluatedthe unit monitors errors, due to measuring inaccuracy of the sensorarrangement, in determination of a position of the unit, as positionalinaccuracy: in a fourth step at least one bonus value and/or penaltyvalue is respectively allotted for each routine of the first, second andthird routines to be carried out, as a function of contribution whichthe at least one bonus value and/or penalty value make in order toenable the unit to reach its destination whereby at least onedestination direction deviation and/or a great positioning error and/ora slight plurality of landmarks are poorly evaluated in the map, aplanning horizon being prescribed as a number of cells to be driventhrough in succession possible travel directions of a unit beingdiscretized such that, starting from a given cell position of the unit,each immediately adjacent cell being reached only in respectively onediscrete travel direction, and for the planning horizon all routes whichcan be combined with using the discrete travel directions, beingevaluated by adding up respectively occurring bonus values and/orpenalty values in the control unit of the unit and that route beingtraveled which achieves a highest bonus value or lowest penalty value;and in a fifth step as a consequence of a common evaluation ofrespective bonus values and/or penalty values in a control unit of theunit at least the routine to be carried out is determined, and a routeof the unit is planned and controlled.
 10. The method as claimed inclaim 9, wherein the degree-of-occupancy values, which are incrementedper cell, are stored as a measure of the probability of occurrence of anobstacle.
 11. The method as claimed in claim 9, wherein the cells aresquare, thereby establishing eight travel directions.
 12. The method asclaimed in claim 9, wherein the cells are hexagonal, therebyestablishing six travel directions.
 13. The method as claimed in claim9, wherein the combined routes are evaluated in a configuration spacewhose space axes are bounded by the planning horizon in two axialdirections, and the number of discrete travel directions is bounded in athird travel direction, common information from information stored inthe cells being stored in the direction of the travel direction axis forall respectively superimposed cells.
 14. The method as claimed in claim13, wherein at least a route cell sequence which achieves a highestbonus value and/or lowest penalty value is stored per cell of theconfiguration space.
 15. The method as claimed in claim 9, wherein aresultant benefit for a route combination is determined as:

    R.sub.N =α.sub.u *B.sub.u +α.sub.c *B.sub.c +α.sub.ƒ *B.sub.ƒ

wherein B is a partial benefit, α is a weighting factors, index u istask of driving to a goals, index c is task of monitoring positionaluncertainty, index f is task of drawing up map,individual partialbenefits being yielded as ##EQU10## wherein N_(ui) (p)=cos(φ) as partialbenefit for the route from one cell to the next cell with partial goal Pand φ as an angle between a discrete travel direction and a start/goaldirection ##EQU11## σ specifying positional uncertainty in thex-direction , σ positional uncertainty in the y-direction and σpositional uncertainty in the φ-direction of rotational orientation ofthe unit ##EQU12## wherein

    N.sub.ƒi =(V.sub.i +T.sub.i +O.sub.i)*C.sub.i

and V_(i) as functional value of a function which, depending on aquantity of the crossing operations in a cell, supplies a real numberwhich continually decreases with increasing quantity, T_(i) as afunctional value of a function which, depending on a quantity of theunconfirmed landmarks which are seen from a cell, supplies a real numberwhich continually increases with increasing quantity, and O_(i) as afunctional value of a function which, depending on correspondencebetween actual travel direction and a travel direction stored for thecell, supplies a real number which continually increases with a traveldirection stored for the cell, and C_(i) as a functional value of afunction which, depending on a quantity of the confirmed landmarks whichare seen from a cell, delivers a continually decreasing real number withincreasing quantity.
 16. The method as claimed in claim 15, wherein thefollowing functions hold: ##EQU13## with Z_(u) as a quantity ofcrossings of the cell considered ##EQU14## with Z_(Lu) as a quantity ofvisible unconfirmed landmarks ##EQU15## with Z_(F) as a quantity ofstored travel directions corresponding to current travel direction of aunit ##EQU16## with Z_(Lb) as a quantity of visible confirmed landmarks.17. The method as claimed in claim 9, wherein a resultant benefit for anindividual cell is calculated as:

    R.sub.i =α.sub.u *B.sub.ui +α.sub.ƒ *B.sub.ƒi +α.sub.c *(1-B.sub.ci).


18. The method as claimed in claim 9, wherein a resultant benefit for anindividual cell is calculated as:

    R.sub.i =(α.sub.u *B.sub.ui +α.sub.ƒ *B.sub.ƒi)*B.sub.ci.